Project ideas

Including Plots

Research question idea: How do different majors vary in the average number of classes taken in each division? How does

# ----CODE FOR TAB NUMBER 3 IN THE SHINY APP----

# here we are mainly just pivoting the major data into a longer format so that it's easier to work with.
majors <- majordata%>%
  pivot_longer(cols = c(`major1`, `major2`, `major3`), names_to = "major#", values_to = "major")%>%
  pivot_longer(cols = c(`major1_division`, `major2_division`, `major3_division`), names_to = "major#_division", values_to = "major_division") %>%
  filter(!is.na(major), !is.na(major_division)) %>%
  filter(`major#` == str_sub(`major#_division`,1,6))%>%
  select("StudentID", "major#", "major", "major#_division", "major_division", "FineArts_count", "Humanities_count", "Interdisciplinary_count", "NatScienceandMath_count", "SocialSciences_count", "NonDivisional_count", "admit_type")%>%
  rename(`Fine Arts` = FineArts_count)%>%
  rename(`Natural Sciences and Mathematics` = NatScienceandMath_count)%>%
  rename(`Humanities` = Humanities_count)%>%
  rename(`Social Sciences` = SocialSciences_count)%>%
  rename(`Interdisciplinary` = Interdisciplinary_count)%>%
  rename(`NonDivisional` = NonDivisional_count)
  
# The number of classes that each student took in the 5 divisions(in long format)
majors2 <- majors%>%
  pivot_longer(cols = c(`Fine Arts`, `Humanities`, `Interdisciplinary`, `Natural Sciences and Mathematics`, `Social Sciences`, `NonDivisional`), names_to = "division", values_to = "numOfClasses")

# This is the average number of classes that a student in each major would take across all of the divisions. 
Divisions_By_Major <- majors%>%
  group_by(major)%>%
  summarise(countFA = mean(`Fine Arts`), countHUM = mean(Humanities), countINT = mean(Interdisciplinary), countSCIMATH = mean(`Natural Sciences and Mathematics`), countSOCSCI = mean(`Social Sciences`), countNON = mean(NonDivisional))

# The average number of classes the a student in each major would take outside of their majors division.
OutsideMajor <- majors2%>%
  filter(!(major_division == division))%>%
  group_by(StudentID, major)%>%
  summarise(num = sum(numOfClasses))

# average number of classes taken outside of your majors division(one number summary). The average number of classes taken outside your majors division is 24.67.
OneNumberSummary <- majors2%>%
  filter(!(admit_type == "TRN"))%>%
  filter(!(major_division == division))%>%
  group_by(StudentID)%>%
  summarise(num = sum(numOfClasses))%>%
  summarise(Average = sum(num)/n())

# average number of classes taken in each division by major, along with the division that each major falls in. 
majorsViz <- OutsideMajor%>%   
  group_by(major)%>%
  summarize(avg = mean(num))%>%
  arrange(desc(avg))

majorsViz <- majorsViz%>% #join with division_by_major
  left_join(majors)%>%
  left_join(Divisions_By_Major)%>%
  distinct(major, .keep_all = TRUE)%>%
  select(major, avg, major_division, countFA, countHUM, countINT, countSCIMATH, countSOCSCI, countNON)

# This is the same data as "majorsViz" above but in the longer format.**This is the data used for the third viz on the shiny app.**
majorsViz2 <- majorsViz%>%
  group_by(major_division)%>%
  pivot_longer(cols = c(countFA, countHUM, countINT, countSCIMATH, countSOCSCI, countNON), names_to = "division", values_to = "avgClasses")

majorsViz2%>%
  group_by(major_division)%>%
  summarise(classesOutside = mean(avg))

#----CODE FOR TAB NUMBER 1 IN THE SHINY APP----
# average number of classes taken in each division based on the number of majors a student has. 
majo <- majordata %>%
  group_by(major_ct) %>%
  summarise(fine_arts = mean(FineArts_count),
            humanities = mean(Humanities_count),
            natscience = mean(NatScienceandMath_count),
            social_science = mean(SocialSciences_count),
            nonDivis = mean(NonDivisional_count),
            inter= mean(Interdisciplinary_count))%>%
  rename(`Fine Arts` = fine_arts)%>%
  rename(`Humanities` = humanities)%>%
  rename(`Social Sciences` =  social_science)%>%
  rename(`Natural Science and Mathematics` = natscience)%>%
  rename(`Non Divisional` = nonDivis)%>%
  rename(`Interdisciplinary` = inter)
# long version of the above.  **Data used for the first viz on the shiny app.**
majjors <- majo %>%
  pivot_longer(cols = !"major_ct", names_to = "division")

#----CODE FOR TAB NUMBER 2 IN THE SHINY APP----
# this is the average number of prefixes for Macalester students(one number summary)
averagePrefix<-majordata %>% 
  summarize("Average Amount of Prefixes of All Students"=mean(prefix_count,na.rm=TRUE),"Medain Prefix"=median(prefix_count))
averagePrefix

#Filtering out all of the students that have more then one major.
singleMajors<-majordata %>% 
  filter(is.na(major2))


singleMajors%>% 
  mutate(across(where(is.character), as.factor)) %>% 
  summary()

#average number of prefixes taken for each major at Macalester.
avgPrefix<-singleMajors %>% 
  group_by(major1) %>% 
  summarize(avgPrefix=mean(prefix_count,na.rm=TRUE)) %>% 
  arrange(desc(avgPrefix)) 

#Same as "avgPrefix", but added a variable for the division that each major falls in. **Data used for the second viz on the shiny app.**
avgPrefix2 <- avgPrefix %>% 
  left_join(singleMajors)%>%
  select(major1, avgPrefix, major1_division)%>%
  distinct()

# ------ PRACTICE VISUALIZATIONS------

#Visualization No. 1
test <- majorsViz2%>%
  ggplot(aes(x = major, y= avgClasses, fill = division))+
  geom_col(position = "fill")+
  theme(axis.text.x = element_text(angle = 90))

#Visualization No. 2***
viz1 <- majorsViz2%>%
  ggplot(aes(x = major, y = avgClasses))+
  geom_point(aes(shape = division, color = major_division))+
  theme(axis.text.x = element_text(angle = 90))+
  labs(title = "Distrubution of Classes Based on Your Major", y = "Average Number of Classes", x = "Major")

#Visualization No. 3
majorsViz2%>%
  ggplot(aes(x = major, y = avgClasses))+
  geom_point(aes(shape = division, color = major_division))+
  theme(axis.text.x = element_text(angle = 90))

ggplotly(test)

ggplotly(viz1)

#This is the custom theme that each one of the visualizations in the shiny app are using. 

theme_gppr <- function(){ 
    font <- "Georgia"   #assign font family up front
    
    theme_minimal() %+replace%    #replace elements we want to change
    
    theme(
      
      #grid elements
      panel.grid.major = element_blank(),    #strip major gridlines
      panel.grid.minor = element_blank(),    #strip minor gridlines
      axis.ticks = element_blank(),          #strip axis ticks
      
      #since theme_minimal() already strips axis lines, 
      #we don't need to do that again
      
      #text elements
      plot.title = element_text(             #title
                   family = font,            #set font family
                   size = 15,                #set font size
                   face = 'bold',            #bold typeface
                   hjust = 0,                #left align
                   vjust = 2),               #raise slightly
      
      plot.subtitle = element_text(          #subtitle
                   family = font,            #font family
                   size = 11),               #font size
      
      plot.caption = element_text(           #caption
                   family = font,            #font family
                   size = 9,                 #font size
                   hjust = 1),               #right align
      
      axis.title = element_text(             #axis titles
                   family = font,            #font family
                   size = 10),               #font size
      
      axis.text = element_text(              #axis text
                   family = font,            #axis famuly
                   size = 9,angle=90),                #font size
      
      axis.text.x = element_text(            #margin for axis text
                    margin=margin(5, b = 10))
      
      #since the legend often requires manual tweaking 
      #based on plot content, don't define it here
    )
}

Project ideas

Nick Kent

2022-11-02

Including Plots